{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 选股_投资广度 \n",
    "从tushare到Alphalens到rqalpha "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import talib as ta\n",
    "import numpy as np\n",
    "\n",
    "# 1. 编制函数需要的算法，调用talib计算momentum\n",
    "def momentum(df, period=2):\n",
    "    return pd.DataFrame(\n",
    "        {name: ta.ROCR(item.values, period) \n",
    "         for name, item in df.iteritems()},\n",
    "         index=df.index\n",
    "        )\n",
    "\n",
    "# 2. 定义计算alpha值的类\n",
    "class alphas(object):\n",
    "    def __init__(self, pn_data):\n",
    "        if pn_data.isnull().values.any():\n",
    "            pn_data.fillna(method='ffill',inplace=True)\n",
    "        self.close = pd.DataFrame(pn_data.minor_xs('close'), \n",
    "                                  dtype=np.float64)\n",
    "\n",
    "# 3. 编制因子的函数，并返回因子DataFrame\n",
    "    def mom001(self):\n",
    "        alpha = -1 * momentum(self.close)\n",
    "        return alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.panel.Panel'>\n",
      "Dimensions: 10 (items) x 971 (major_axis) x 6 (minor_axis)\n",
      "Items axis: 000001 to 601318\n",
      "Major_axis axis: 2013-01-04 00:00:00 to 2016-12-30 00:00:00\n",
      "Minor_axis axis: open to code\n"
     ]
    }
   ],
   "source": [
    "# 4. 传入股票数据\n",
    "if __name__ == '__main__':\n",
    "    import pandas as pd\n",
    "    import tushare as ts\n",
    "\n",
    "    codes = ['000001', '601318', '600029', '000089', '000402', \n",
    "             '000895', '600006', '000858', '600036', '600050']\n",
    "    stocks_dict = {}\n",
    "    for c in codes:\n",
    "        stock = ts.get_k_data(c, start='2013-01-01', end='2016-12-31', ktype='D', autype='qfq')\n",
    "        stock.index = pd.to_datetime(stock['date'], format='%Y-%m-%d')\n",
    "        stock.pop('date')\n",
    "        stocks_dict[c] = stock\n",
    "\n",
    "    pn = pd.Panel(stocks_dict)\n",
    "    print pn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              000001    000089    000402    000858    000895    600006  \\\n",
      "date                                                                     \n",
      "2016-12-26 -0.997812 -0.993865 -1.025000 -0.970079 -0.983686 -0.980716   \n",
      "2016-12-27 -1.000000 -1.004957 -1.021236 -0.997687 -0.999486 -0.988764   \n",
      "2016-12-28 -0.993421 -0.983951 -0.978424 -0.987193 -0.995092 -0.973315   \n",
      "2016-12-29 -1.000000 -0.974106 -0.977316 -0.985733 -1.005353 -0.982955   \n",
      "2016-12-30 -1.004415 -1.003764 -0.987536 -1.016796 -1.026532 -0.992785   \n",
      "\n",
      "              600029    600036    600050    601318  \n",
      "date                                                \n",
      "2016-12-26 -0.988811 -1.001693 -1.016927 -1.010066  \n",
      "2016-12-27 -0.985975 -1.006250 -0.966709 -1.015261  \n",
      "2016-12-28 -0.985856 -0.992676 -0.984635 -1.004841  \n",
      "2016-12-29 -0.994310 -0.987578 -0.998675 -0.996313  \n",
      "2016-12-30 -1.007174 -0.998865 -0.950585 -1.003967  \n"
     ]
    }
   ],
   "source": [
    "#5. 输出Factor数据\n",
    "alpha_mom = alphas(pn).mom001()\n",
    "print alpha_mom.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date              \n",
      "2016-12-30  600006   -0.992785\n",
      "            600029   -1.007174\n",
      "            600036   -0.998865\n",
      "            600050   -0.950585\n",
      "            601318   -1.003967\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#转换成MultiIndex\n",
    "factor = alpha_mom.stack()\n",
    "print factor.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           000001 000089 000402  000858  000895 600006 600029 600036 600050  \\\n",
      "date                                                                          \n",
      "2016-12-26   9.12    8.1  10.66  33.653  19.355   7.12   7.07  17.75   7.81   \n",
      "2016-12-27   9.08   8.11  10.58  33.643   19.43   7.04   7.03  17.71   7.55   \n",
      "2016-12-28   9.06   7.97  10.43  33.222   19.26   6.93   6.97  17.62   7.69   \n",
      "2016-12-29   9.08    7.9  10.34  33.163  19.534   6.92   6.99  17.49   7.54   \n",
      "2016-12-30    9.1      8   10.3   33.78  19.771   6.88   7.02   17.6   7.31   \n",
      "\n",
      "           601318  \n",
      "date               \n",
      "2016-12-26  35.12  \n",
      "2016-12-27  35.26  \n",
      "2016-12-28  35.29  \n",
      "2016-12-29  35.13  \n",
      "2016-12-30  35.43  \n"
     ]
    }
   ],
   "source": [
    "# 股票池价格的Dataframe\n",
    "prices = pn.minor_xs('close')\n",
    "print prices.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          1         5        10    factor  factor_quantile\n",
      "date       asset                                                          \n",
      "2013-01-08 000001 -0.008869  0.100633  0.280000 -1.000725                3\n",
      "           000089 -0.002677  0.018201  0.065043 -1.000000                3\n",
      "           000402 -0.005838  0.001501  0.007339 -0.989764                4\n",
      "           000858  0.017792  0.014242 -0.062885 -1.008229                1\n",
      "           000895 -0.000809  0.049779  0.044055 -1.076495                1\n"
     ]
    }
   ],
   "source": [
    "#输入Alphalen所需要的数据格式\n",
    "import alphalens\n",
    "\n",
    "factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factor, prices, quantiles=5)\n",
    "print factor_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asset      000001 000089 000402 000858 000895 600006 600029 600036 600050  \\\n",
      "date                                                                        \n",
      "2013-01-08  False  False  False   True   True  False  False  False  False   \n",
      "2013-01-09  False  False  False   True   True  False  False  False  False   \n",
      "2013-01-10  False  False  False   True  False  False   True  False  False   \n",
      "2013-01-11  False  False  False   True  False  False   True  False  False   \n",
      "2013-01-14   True  False  False  False   True  False  False  False  False   \n",
      "2013-01-15   True  False  False  False  False  False  False  False  False   \n",
      "2013-01-16   True  False  False  False   True  False  False  False  False   \n",
      "2013-01-17  False   True  False  False   True  False  False  False  False   \n",
      "2013-01-18  False   True   True  False  False  False  False  False  False   \n",
      "2013-01-21   True  False  False  False  False  False   True  False  False   \n",
      "2013-01-22   True  False  False  False  False  False  False   True  False   \n",
      "2013-01-23  False  False  False  False   True  False  False   True  False   \n",
      "2013-01-24  False  False  False  False   True  False  False  False  False   \n",
      "2013-01-25  False  False  False  False   True   True  False  False  False   \n",
      "2013-01-28   True  False  False  False  False   True  False  False  False   \n",
      "2013-01-29   True  False  False  False  False  False  False   True  False   \n",
      "2013-01-30  False  False  False   True  False  False  False  False  False   \n",
      "2013-01-31  False  False  False  False  False  False   True  False  False   \n",
      "2013-02-01   True  False  False  False  False  False  False  False  False   \n",
      "2013-02-04  False  False  False  False  False  False  False  False   True   \n",
      "2013-02-05  False  False  False   True  False  False  False  False   True   \n",
      "2013-02-06  False  False  False   True  False  False  False  False  False   \n",
      "2013-02-07  False  False  False  False   True  False  False  False  False   \n",
      "2013-02-08  False  False   True  False   True  False  False  False  False   \n",
      "2013-02-18   True  False  False  False  False   True  False  False  False   \n",
      "2013-02-19   True  False  False  False   True  False  False  False  False   \n",
      "2013-02-20  False  False  False  False   True  False  False   True  False   \n",
      "2013-02-21  False  False  False   True   True  False  False  False  False   \n",
      "2013-02-22  False  False  False   True  False  False  False  False   True   \n",
      "2013-02-25   True  False  False   True  False  False  False  False  False   \n",
      "...           ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
      "2016-11-07  False  False   True  False  False  False  False  False   True   \n",
      "2016-11-08  False   True  False   True  False  False  False  False  False   \n",
      "2016-11-09  False   True  False   True  False  False  False  False  False   \n",
      "2016-11-10  False  False   True  False  False  False   True  False  False   \n",
      "2016-11-11  False  False  False  False  False  False   True  False   True   \n",
      "2016-11-14  False  False  False  False  False  False   True  False   True   \n",
      "2016-11-15  False  False  False  False  False  False   True  False   True   \n",
      "2016-11-16  False  False   True  False  False  False  False  False   True   \n",
      "2016-11-17  False  False  False  False  False  False   True  False   True   \n",
      "2016-11-18  False  False   True  False  False  False  False  False   True   \n",
      "2016-11-21  False  False  False  False  False  False  False   True  False   \n",
      "2016-11-22   True  False  False  False  False  False  False  False  False   \n",
      "2016-11-23  False  False   True  False  False  False  False   True  False   \n",
      "2016-11-24  False  False   True  False   True  False  False  False  False   \n",
      "2016-11-25  False  False   True  False   True  False  False  False  False   \n",
      "2016-11-28  False   True  False  False  False  False  False  False   True   \n",
      "2016-11-29  False  False  False   True  False  False   True  False  False   \n",
      "2016-11-30  False  False  False  False  False  False   True  False   True   \n",
      "2016-12-01  False  False   True  False  False  False  False  False   True   \n",
      "2016-12-02  False  False   True  False  False  False  False  False   True   \n",
      "2016-12-05  False  False  False  False  False  False  False   True  False   \n",
      "2016-12-06  False  False  False   True  False  False  False   True  False   \n",
      "2016-12-07  False   True  False   True  False  False  False  False  False   \n",
      "2016-12-08  False  False  False   True  False  False  False  False   True   \n",
      "2016-12-09  False  False  False  False  False  False  False   True   True   \n",
      "2016-12-12  False  False  False  False  False  False  False   True  False   \n",
      "2016-12-13  False  False  False  False  False  False  False  False   True   \n",
      "2016-12-14  False  False  False  False  False  False  False  False   True   \n",
      "2016-12-15   True  False  False  False  False   True  False  False  False   \n",
      "2016-12-16  False  False  False  False   True   True  False  False  False   \n",
      "\n",
      "asset      601318  \n",
      "date               \n",
      "2013-01-08  False  \n",
      "2013-01-09  False  \n",
      "2013-01-10  False  \n",
      "2013-01-11  False  \n",
      "2013-01-14  False  \n",
      "2013-01-15   True  \n",
      "2013-01-16  False  \n",
      "2013-01-17  False  \n",
      "2013-01-18  False  \n",
      "2013-01-21  False  \n",
      "2013-01-22  False  \n",
      "2013-01-23  False  \n",
      "2013-01-24   True  \n",
      "2013-01-25  False  \n",
      "2013-01-28  False  \n",
      "2013-01-29  False  \n",
      "2013-01-30   True  \n",
      "2013-01-31   True  \n",
      "2013-02-01   True  \n",
      "2013-02-04   True  \n",
      "2013-02-05  False  \n",
      "2013-02-06   True  \n",
      "2013-02-07   True  \n",
      "2013-02-08  False  \n",
      "2013-02-18  False  \n",
      "2013-02-19  False  \n",
      "2013-02-20  False  \n",
      "2013-02-21  False  \n",
      "2013-02-22  False  \n",
      "2013-02-25  False  \n",
      "...           ...  \n",
      "2016-11-07  False  \n",
      "2016-11-08  False  \n",
      "2016-11-09  False  \n",
      "2016-11-10  False  \n",
      "2016-11-11  False  \n",
      "2016-11-14  False  \n",
      "2016-11-15  False  \n",
      "2016-11-16  False  \n",
      "2016-11-17  False  \n",
      "2016-11-18  False  \n",
      "2016-11-21   True  \n",
      "2016-11-22   True  \n",
      "2016-11-23  False  \n",
      "2016-11-24  False  \n",
      "2016-11-25  False  \n",
      "2016-11-28  False  \n",
      "2016-11-29  False  \n",
      "2016-11-30  False  \n",
      "2016-12-01  False  \n",
      "2016-12-02  False  \n",
      "2016-12-05   True  \n",
      "2016-12-06  False  \n",
      "2016-12-07  False  \n",
      "2016-12-08  False  \n",
      "2016-12-09  False  \n",
      "2016-12-12   True  \n",
      "2016-12-13   True  \n",
      "2016-12-14   True  \n",
      "2016-12-15  False  \n",
      "2016-12-16  False  \n",
      "\n",
      "[959 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "cond = factor_data['factor_quantile'] == 1\n",
    "save = factor_data[cond]\n",
    "s = pd.Series(True, index=save.index)\n",
    "s = s.unstack()\n",
    "s[s != True] = False\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 得出表格后转名字并用到rqalpha测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           000001.XSHE 000089.XSHE 000402.XSHE 000858.XSHE 000895.XSHE  \\\n",
      "date                                                                     \n",
      "2013-01-08       False       False       False        True        True   \n",
      "2013-01-09       False       False       False        True        True   \n",
      "2013-01-10       False       False       False        True       False   \n",
      "2013-01-11       False       False       False        True       False   \n",
      "2013-01-14        True       False       False       False        True   \n",
      "2013-01-15        True       False       False       False       False   \n",
      "2013-01-16        True       False       False       False        True   \n",
      "2013-01-17       False        True       False       False        True   \n",
      "2013-01-18       False        True        True       False       False   \n",
      "2013-01-21        True       False       False       False       False   \n",
      "2013-01-22        True       False       False       False       False   \n",
      "2013-01-23       False       False       False       False        True   \n",
      "2013-01-24       False       False       False       False        True   \n",
      "2013-01-25       False       False       False       False        True   \n",
      "2013-01-28        True       False       False       False       False   \n",
      "2013-01-29        True       False       False       False       False   \n",
      "2013-01-30       False       False       False        True       False   \n",
      "2013-01-31       False       False       False       False       False   \n",
      "2013-02-01        True       False       False       False       False   \n",
      "2013-02-04       False       False       False       False       False   \n",
      "2013-02-05       False       False       False        True       False   \n",
      "2013-02-06       False       False       False        True       False   \n",
      "2013-02-07       False       False       False       False        True   \n",
      "2013-02-08       False       False        True       False        True   \n",
      "2013-02-18        True       False       False       False       False   \n",
      "2013-02-19        True       False       False       False        True   \n",
      "2013-02-20       False       False       False       False        True   \n",
      "2013-02-21       False       False       False        True        True   \n",
      "2013-02-22       False       False       False        True       False   \n",
      "2013-02-25        True       False       False        True       False   \n",
      "...                ...         ...         ...         ...         ...   \n",
      "2016-11-07       False       False        True       False       False   \n",
      "2016-11-08       False        True       False        True       False   \n",
      "2016-11-09       False        True       False        True       False   \n",
      "2016-11-10       False       False        True       False       False   \n",
      "2016-11-11       False       False       False       False       False   \n",
      "2016-11-14       False       False       False       False       False   \n",
      "2016-11-15       False       False       False       False       False   \n",
      "2016-11-16       False       False        True       False       False   \n",
      "2016-11-17       False       False       False       False       False   \n",
      "2016-11-18       False       False        True       False       False   \n",
      "2016-11-21       False       False       False       False       False   \n",
      "2016-11-22        True       False       False       False       False   \n",
      "2016-11-23       False       False        True       False       False   \n",
      "2016-11-24       False       False        True       False        True   \n",
      "2016-11-25       False       False        True       False        True   \n",
      "2016-11-28       False        True       False       False       False   \n",
      "2016-11-29       False       False       False        True       False   \n",
      "2016-11-30       False       False       False       False       False   \n",
      "2016-12-01       False       False        True       False       False   \n",
      "2016-12-02       False       False        True       False       False   \n",
      "2016-12-05       False       False       False       False       False   \n",
      "2016-12-06       False       False       False        True       False   \n",
      "2016-12-07       False        True       False        True       False   \n",
      "2016-12-08       False       False       False        True       False   \n",
      "2016-12-09       False       False       False       False       False   \n",
      "2016-12-12       False       False       False       False       False   \n",
      "2016-12-13       False       False       False       False       False   \n",
      "2016-12-14       False       False       False       False       False   \n",
      "2016-12-15        True       False       False       False       False   \n",
      "2016-12-16       False       False       False       False        True   \n",
      "\n",
      "           600006.XSHG 600029.XSHG 600036.XSHG 600050.XSHG 601318.XSHG  \n",
      "date                                                                    \n",
      "2013-01-08       False       False       False       False       False  \n",
      "2013-01-09       False       False       False       False       False  \n",
      "2013-01-10       False        True       False       False       False  \n",
      "2013-01-11       False        True       False       False       False  \n",
      "2013-01-14       False       False       False       False       False  \n",
      "2013-01-15       False       False       False       False        True  \n",
      "2013-01-16       False       False       False       False       False  \n",
      "2013-01-17       False       False       False       False       False  \n",
      "2013-01-18       False       False       False       False       False  \n",
      "2013-01-21       False        True       False       False       False  \n",
      "2013-01-22       False       False        True       False       False  \n",
      "2013-01-23       False       False        True       False       False  \n",
      "2013-01-24       False       False       False       False        True  \n",
      "2013-01-25        True       False       False       False       False  \n",
      "2013-01-28        True       False       False       False       False  \n",
      "2013-01-29       False       False        True       False       False  \n",
      "2013-01-30       False       False       False       False        True  \n",
      "2013-01-31       False        True       False       False        True  \n",
      "2013-02-01       False       False       False       False        True  \n",
      "2013-02-04       False       False       False        True        True  \n",
      "2013-02-05       False       False       False        True       False  \n",
      "2013-02-06       False       False       False       False        True  \n",
      "2013-02-07       False       False       False       False        True  \n",
      "2013-02-08       False       False       False       False       False  \n",
      "2013-02-18        True       False       False       False       False  \n",
      "2013-02-19       False       False       False       False       False  \n",
      "2013-02-20       False       False        True       False       False  \n",
      "2013-02-21       False       False       False       False       False  \n",
      "2013-02-22       False       False       False        True       False  \n",
      "2013-02-25       False       False       False       False       False  \n",
      "...                ...         ...         ...         ...         ...  \n",
      "2016-11-07       False       False       False        True       False  \n",
      "2016-11-08       False       False       False       False       False  \n",
      "2016-11-09       False       False       False       False       False  \n",
      "2016-11-10       False        True       False       False       False  \n",
      "2016-11-11       False        True       False        True       False  \n",
      "2016-11-14       False        True       False        True       False  \n",
      "2016-11-15       False        True       False        True       False  \n",
      "2016-11-16       False       False       False        True       False  \n",
      "2016-11-17       False        True       False        True       False  \n",
      "2016-11-18       False       False       False        True       False  \n",
      "2016-11-21       False       False        True       False        True  \n",
      "2016-11-22       False       False       False       False        True  \n",
      "2016-11-23       False       False        True       False       False  \n",
      "2016-11-24       False       False       False       False       False  \n",
      "2016-11-25       False       False       False       False       False  \n",
      "2016-11-28       False       False       False        True       False  \n",
      "2016-11-29       False        True       False       False       False  \n",
      "2016-11-30       False        True       False        True       False  \n",
      "2016-12-01       False       False       False        True       False  \n",
      "2016-12-02       False       False       False        True       False  \n",
      "2016-12-05       False       False        True       False        True  \n",
      "2016-12-06       False       False        True       False       False  \n",
      "2016-12-07       False       False       False       False       False  \n",
      "2016-12-08       False       False       False        True       False  \n",
      "2016-12-09       False       False        True        True       False  \n",
      "2016-12-12       False       False        True       False        True  \n",
      "2016-12-13       False       False       False        True        True  \n",
      "2016-12-14       False       False       False        True        True  \n",
      "2016-12-15        True       False       False       False       False  \n",
      "2016-12-16        True       False       False       False       False  \n",
      "\n",
      "[959 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "def coder(code):\n",
    "    if code.startswith('6'):\n",
    "        return code + '.XSHG'\n",
    "    elif code.startswith('3') or code.startswith('0'):\n",
    "        return code + '.XSHE'\n",
    "    else:\n",
    "        return code\n",
    "\n",
    "codes = s.columns\n",
    "new_codes = []\n",
    "for code in codes:\n",
    "    c = coder(code)\n",
    "    new_codes.append(c)\n",
    "\n",
    "s.columns = new_codes\n",
    "print s\n",
    "s.to_csv('C:\\\\Users\\\\small\\\\Desktop\\\\alpha_stocks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9e4Qf2I+i++9Hkn3lyiRGRXH3VBAl69TVf69Sq6n20QA0jx/TfO7PFKlYiZrDR1C1vzcO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\n1PNtsk6tRKGQcHl7T9K3pG0ft921mtbdu/hsWwkulZ7LVl+AVqMi6HQSP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tkp4e0TZ48z\nJMtsPtTASx8dp6+nskKCws4yFlduREKmXWsl3teJvOAiXJ4g6tI9bE+7gLq4dNzqcKRChtrH3JlZ\n6DVKPtlUgk13ehtdvLeD6e1HybeVMffO2wlZEvnLXzbgNlhoi0+n1d/dWZRm1jBnajJtdm+kmoTi\nVLWTSYkGiqclseFAA5/uryMkg9Wo4criSSwpSCHeoMHh9nOwop26Vic2p4/jtZ0oFRLZKUb0GiUG\nnQqDRoVBp2JGloXy+k62ljRyssHRrVpMdnIcNy2bHKlYUdfmoq3TS5vDS3WzE5vDS5vDF9lqRbiL\nfGFhFr5AiNJqGzXNTtQqBYnxWgJBmeYODwAJJi0/+sqCfpPzdtNEzM0xQ2wJiF2bvSH0jD17AyG2\nBMSezf4QesaOvaEgtniMIcJB0Z2JMPnHwuaWww28uO44wVMeiTsuncqyolRSU9M4UVGNQiFFygUm\nGDX85OuLumXSb7K52VbShNWoISfVSG6qCVmW2VvWytvbq0iz6rmwMJU5ef1vxzgf72207Z1Nb3PU\n4fZzstFOk81DmlWPxxfkjW2V1Le5CMkyVy3K4saLJne7psnmpsPpQ6tWotMo0aq7/lMgSdKA4/z9\n+6VsPZUc1aBVUZRrpcPp61bVwSK7MbjaqYvr+0GvIYBfVvRIVAqgC3hY4T9Okd6FISWFpEWLIyVf\nQ34/kiq8OD9c2c7+0npUPhc7G4K02n092uoNSQKtSonHH3buKBUSmYkGqpqHn3BSp1EyNT2e43Ud\nPUoW90WCSYtGpaC5wxOZw2f28ey/fksKUrhl+WQsxsHnPxFzc+wQC9rYtdkbQs/YszcQYkEbezb7\nQ+gZO/aGgnBQjCHCQdGdiTD5x8qm2xvA7vYTCMpkJJ56K32GniFZprbFRYJJM6Skd0PhfL230bR3\nNkOZo4FgCH8whF4zuLfsZzLQOL3+IMdrO7C7AxRmWzDHhcvIttm9tDt8/PLNw9jd/m7XJJt1KCRo\ntIWjAL6xejqL88w0HTjE+rc30u6T8Cq1mEIebvzOl4hLSUGvH1oSUl8gxO7jzWw+1IhRp2LR9GQU\nkoQMON1+tpc20Wr3kp5g4KaLJpOeYODdnVUcr+mgpNoWcQYkm3Usnp6MOU6Dzekj0aRFo1bg9gap\nbXWy5XAjgaAcqRwzNy+BnFQjM3OsWI1a3N4Av3zrMCcb7FiMWoLBECqlglSrnumZZiYlx5EUr6Mg\nP4eOtmYgHM206WADpTUdpFr0zMgyk5ceT5PNjdMTwGxQo9eqMMf1rKgxEGJujh1iQRu7NntD6Bl7\n9gZCLGhjz2Z/CD1jx95QEA6KMUQ4KLozESZ/tGyC0PN8sHc2Y6XpuY6zyebm1c0VpCboueDUQt+o\nV+PzB/nFm4fx+IM8dMscVMqwnaDXi/NkObIso01KRpd87kkaB8OZ42x3eDlRbycrKY7UAcrddiXE\nHOgcoN/zYkXPWLAZ7bnZhVjQxq7N3hB6xp69gRAL2tiz2R9Cz9ixNxRiwUEx9Nd/45SuGzTS544k\nY2k3GmOcKDajYXsi3NtoajmWfThXG2kJcXz32pk9juu0Cn5wy9ye9vR6EopmAdH5MgCQGK8nMX7g\nhJOj2YdYthFtm+NhbnYxmn2ZCFpGy2ZfCD1jy95gGI0+TQQto2VzIISesWFvqIxU/0ZrnOP77g3A\n7t27ee6556LdDYFAIBAIBAKBQCAQCATnSExHUBQXF1NcXAwM783gWIfdRCPMR9gUNmPNZjTD4cbS\n9kTQMlo2o2F7Itzb8RCqOhZ9mAhaRstmNPowEe7teNCyi9Hsy0TQMlo2+0LoGVv2BstI92uk24vp\nCAqBQCAQCAQCgUAgEAgE5wfCQSEQCAQCgUAgEAgEAoEg6ggHhUAgEAgEAoFAIBAIBIKoE9M5KM5E\nVPGInq2JZjMatifCvR0PGY9F1YfYthmNPkyEezsetOxCVH2ITZt9IfSMLXuDQVR9iC2bAyH0jA17\nQ0VU8RhFRBUPgUAgEAgEAoFAIBAIzg8kWZblaHdCIBAIBAKBQCAQCAQCwcQmpiMozoWxjrz4wQ9+\nMKb2YOzHOJFsCj3PD3tnMpaaTgQto2WzC6FnbNvrjbHQdCJoGS2bZyP0jE17/TGamk4ELaNlsy+E\nnrFlb7CMtK6jMc4J66BYsGBBtLsw6kRjjBPFZjSYCPdWaClsxiIT4d4KLYXNWGQi3FuhpbAZi0yE\neyu0HD4T1kFRXFwc7S6MOtEY40SxGQ0mwr0VWgqbschEuLdCS2EzFpkI91ZoKWzGIhPh3goth49y\nzZo1a0a8VUGv5OXlRbsLghFE6Hn+ITQ9vxB6nn8ITc8vhJ7nH0LT8wuh5/nJeNdVJMkUCAQCgUAg\nEAgEAoFAEHUm7BaPscLhcHDgwAE6Ozuj3RXBCCD0FAgEAoFAIBAIBILRQWzxGAQul4unn36aDRs2\nsHPnThYvXsxzzz3HW2+9RXt7O4WFhQDYbDZ+/OMfc8kllwDQ3t7OU089hU6n4+WXX2bJkiVotdpe\nbTz77LMDticYGaKh57p163j55ZfZsGED7733HhUVFRMmec5YMFxNu7DZbDz66KNcccUVfdoQc3Ts\niIaeYo6OLsPVNBgM8m//9m/s3r2bDRs2kJeXh9ls7tWGmKNjRzT0FHN0dDnX5+7zzz9PKBQiIyOj\nTxtijo4d0dBTzNHRZbiaDkWX8TJHRQTFINi8eTNXXXUVjzzyCBaLha1btxIKhXj88cdpb2+nvr4e\nh8PBM888g9frjVxXXV3NnXfeyQ033MCcOXMoLy/vtf0dO3YMqj3ByBANPVeuXMmaNWtYs2YNBQUF\nXHbZZWM13AnBcDXt4uWXX8bn8/XZvpijY0s09BRzdHQZrqaVlZUsXbo0ok12dnav7Ys5OrZEQ08x\nR0eXc3nuHjlyBJvN1m+yPDFHx5Zo6Cnm6OgyXE0Hq8t4mqPCQTEIVq1axezZswHo7Oxk8+bNLFmy\nBICioiJKS0tRKBTcd9996PX6yHWzZ89m2rRplJSUcOLECaZNm9Zr+4cPHx5Ue4KRIRp6dtHW1obN\nZmPKlCmjNbwJyXA1BTh06BBarRaLxdJn+2KOji3R0LMLMUdHh+Fqevz4cXbt2sUjjzzC2rVrCQaD\nvbYv5ujYEg09uxBzdHQYrqaBQIDnnnuO5ORkdu3a1Wf7Yo6OLdHQswsxR0eHc/luBAPrMp7mqGpM\nrcU4x44dw+l0kpycTEJCAgB6vZ6GhgYMBkOv18iyzLZt21AqlSgUCn73u99RV1cX+XlRURFer3fQ\n7QlGjrHUs4sPPviAlStXjuKoJjZD1TQQCPDqq6/ywAMP8PTTTwOIOTqOGEs9uxBzdHQZqqZTpkxh\nzZo1WK1Wnn/+eT7//HP27t0r5ug4YSz17ELM0dFlqJpu2rSJSZMmce211/L+++/T0tJCdXW1mKPj\nhLHUswsxR0eX4axfoLsu4/27rnBQDBKHw8ELL7zA/fffz7vvvhsJH/Z4PIRCoT6vkySJu+++m1de\neYW9e/fyzW9+s8c5L7744qDbE4wM0dAzFApx+PBhbrvttlEYkWA4mr755pusWrWKuLi4yDExR8cH\n0dBTzNHRZTia5uTkoFarAcjMzKS+vl7M0XFCNPQUc3R0GY6mJ0+e5PLLL8disbBs2TJeeeUV/vM/\n/7PHeWKOjj3R0FPM0dFluOuXs3UZ739HxRaPQRAIBPj5z3/Ol7/8ZZKTk8nLy4uEMlVWVpKSktLr\ndW+++SYbN24EwolN+vJCDbY9wcgQLT1LS0vJz89HkqSRHtKEZ7iaHjx4kA8//JA1a9ZQUVHBb3/7\n217PE3N0bImWnmKOjh7D1fRXv/oVFRUVhEIhdu7cSU5OTq/niTk6tkRLTzFHR4/hapqWlkZjYyMA\n5eXlJCUl9XqemKNjS7T0FHN09BiupjA4XcbTHBURFINg/fr1lJeX8/rrr/P6669z8cUXs3nzZtrb\n29m3bx9PPPFEr9ddfvnl/PznP2f9+vVkZWUxZ86cXs9buHAhP/zhDwdsTzAyREvPffv2UVBQMGrj\nmsgMV9PHHnss8u81a9bw7W9/u9fzxBwdW6Klp5ijo8dwNb3ppptYu3YtsixTXFwc2X97NmKOji3R\n0lPM0dFjuJpeeumlPPvss2zbto1AIMD999/f63lijo4t0dJTzNHRY7iawuB0GU9zVJJlWY6a9RjG\n4XBw4MABCgsL+03GFq32BEND6Hn+ITQ9vxB6nn8ITc8vhJ7nH0LT8wuh5/nH+aqpcFAIBAKBQCAQ\nCAQCgUAgiDoiB4VAIBAIBAKBQCAQCASCqCMcFAKBQCAQCAQCgUAgEAiijnBQCAQCgUAgEAgEAoFA\nIIg6wkEhEAgEAoFAIBAIBAKBIOoIB4VAIBAIBAKBQCAQCASCqCMcFAKBQCAQCAQCgUAgEAiizv8H\nR8zfCl4ZY14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x330cdd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'sys_analyser': {'benchmark_portfolio':                cash  market_value  static_unit_net_value  total_value  \\\n",
       "  date                                                                    \n",
       "  2013-01-08  1512.13      98487.87                  1.000    100000.00   \n",
       "  2013-01-09  1512.13      98519.07                  1.000    100031.20   \n",
       "  2013-01-10  1512.13      98692.23                  1.000    100204.36   \n",
       "  2013-01-11  1512.13      96845.97                  1.002     98358.10   \n",
       "  2013-01-14  1512.13     100531.08                  0.984    102043.21   \n",
       "  2013-01-15  1512.13     101238.54                  1.020    102750.67   \n",
       "  2013-01-16  1512.13     100506.51                  1.028    102018.64   \n",
       "  2013-01-17  1512.13      99557.64                  1.020    101069.77   \n",
       "  2013-01-18  1512.13     101222.16                  1.011    102734.29   \n",
       "  2013-01-21  1512.13     101825.10                  1.027    103337.23   \n",
       "  2013-01-22  1512.13     101279.10                  1.033    102791.23   \n",
       "  2013-01-23  1512.13     101690.94                  1.028    103203.07   \n",
       "  2013-01-24  1512.13     100727.64                  1.032    102239.77   \n",
       "  2013-01-25  1512.13     100295.13                  1.022    101807.26   \n",
       "  2013-01-28  1512.13     103422.54                  1.018    104934.67   \n",
       "  2013-01-29  1512.13     104358.93                  1.049    105871.06   \n",
       "  2013-01-30  1512.13     104859.69                  1.059    106371.82   \n",
       "  2013-01-31  1512.13     104788.32                  1.064    106300.45   \n",
       "  2013-02-01  1512.13     106989.48                  1.063    108501.61   \n",
       "  2013-02-04  1512.13     107173.17                  1.085    108685.30   \n",
       "  2013-02-05  1512.13     108095.52                  1.087    109607.65   \n",
       "  2013-02-06  1512.13     108257.76                  1.096    109769.89   \n",
       "  2013-02-07  1512.13     107634.93                  1.098    109147.06   \n",
       "  2013-02-08  1512.13     108097.08                  1.091    109609.21   \n",
       "  2013-02-18  1512.13     106761.33                  1.096    108273.46   \n",
       "  2013-02-19  1512.13     104738.79                  1.083    106250.92   \n",
       "  2013-02-20  1512.13     105402.96                  1.063    106915.09   \n",
       "  2013-02-21  1512.13     101811.45                  1.069    103323.58   \n",
       "  2013-02-22  1512.13     101267.40                  1.033    102779.53   \n",
       "  2013-02-25  1512.13     101593.44                  1.028    103105.57   \n",
       "  ...             ...           ...                    ...          ...   \n",
       "  2016-10-31  1512.13     130114.92                  1.318    131627.05   \n",
       "  2016-11-01  1512.13     131002.95                  1.316    132515.08   \n",
       "  2016-11-02  1512.13     130000.65                  1.325    131512.78   \n",
       "  2016-11-03  1512.13     131238.12                  1.315    132750.25   \n",
       "  2016-11-04  1512.13     130812.63                  1.328    132324.76   \n",
       "  2016-11-07  1512.13     130907.01                  1.323    132419.14   \n",
       "  2016-11-08  1512.13     131473.68                  1.324    132985.81   \n",
       "  2016-11-09  1512.13     130768.95                  1.330    132281.08   \n",
       "  2016-11-10  1512.13     132233.79                  1.323    133745.92   \n",
       "  2016-11-11  1512.13     133271.58                  1.337    134783.71   \n",
       "  2016-11-14  1512.13     133779.75                  1.348    135291.88   \n",
       "  2016-11-15  1512.13     133764.93                  1.353    135277.06   \n",
       "  2016-11-16  1512.13     133754.01                  1.353    135266.14   \n",
       "  2016-11-17  1512.13     134025.06                  1.353    135537.19   \n",
       "  2016-11-18  1512.13     133280.94                  1.355    134793.07   \n",
       "  2016-11-21  1512.13     134203.29                  1.348    135715.42   \n",
       "  2016-11-22  1512.13     135266.04                  1.357    136778.17   \n",
       "  2016-11-23  1512.13     135514.47                  1.368    137026.60   \n",
       "  2016-11-24  1512.13     136060.86                  1.370    137572.99   \n",
       "  2016-11-25  1512.13     137330.70                  1.376    138842.83   \n",
       "  2016-11-28  1512.13     137868.12                  1.388    139380.25   \n",
       "  2016-11-29  1512.13     138997.56                  1.394    140509.69   \n",
       "  2016-11-30  1512.13     137982.00                  1.405    139494.13   \n",
       "  2016-12-01  1512.13     139036.56                  1.395    140548.69   \n",
       "  2016-12-02  1512.13     137629.05                  1.405    139141.18   \n",
       "  2016-12-05  1512.13     135306.99                  1.391    136819.12   \n",
       "  2016-12-06  1512.13     134906.85                  1.368    136418.98   \n",
       "  2016-12-07  1512.13     135554.25                  1.364    137066.38   \n",
       "  2016-12-08  1512.13     135335.46                  1.371    136847.59   \n",
       "  2016-12-09  1512.13     136254.30                  1.368    137766.43   \n",
       "  \n",
       "              unit_net_value   units  \n",
       "  date                                \n",
       "  2013-01-08        1.000000  100000  \n",
       "  2013-01-09        1.000312  100000  \n",
       "  2013-01-10        1.002044  100000  \n",
       "  2013-01-11        0.983581  100000  \n",
       "  2013-01-14        1.020432  100000  \n",
       "  2013-01-15        1.027507  100000  \n",
       "  2013-01-16        1.020186  100000  \n",
       "  2013-01-17        1.010698  100000  \n",
       "  2013-01-18        1.027343  100000  \n",
       "  2013-01-21        1.033372  100000  \n",
       "  2013-01-22        1.027912  100000  \n",
       "  2013-01-23        1.032031  100000  \n",
       "  2013-01-24        1.022398  100000  \n",
       "  2013-01-25        1.018073  100000  \n",
       "  2013-01-28        1.049347  100000  \n",
       "  2013-01-29        1.058711  100000  \n",
       "  2013-01-30        1.063718  100000  \n",
       "  2013-01-31        1.063005  100000  \n",
       "  2013-02-01        1.085016  100000  \n",
       "  2013-02-04        1.086853  100000  \n",
       "  2013-02-05        1.096076  100000  \n",
       "  2013-02-06        1.097699  100000  \n",
       "  2013-02-07        1.091471  100000  \n",
       "  2013-02-08        1.096092  100000  \n",
       "  2013-02-18        1.082735  100000  \n",
       "  2013-02-19        1.062509  100000  \n",
       "  2013-02-20        1.069151  100000  \n",
       "  2013-02-21        1.033236  100000  \n",
       "  2013-02-22        1.027795  100000  \n",
       "  2013-02-25        1.031056  100000  \n",
       "  ...                    ...     ...  \n",
       "  2016-10-31        1.316271  100000  \n",
       "  2016-11-01        1.325151  100000  \n",
       "  2016-11-02        1.315128  100000  \n",
       "  2016-11-03        1.327503  100000  \n",
       "  2016-11-04        1.323248  100000  \n",
       "  2016-11-07        1.324191  100000  \n",
       "  2016-11-08        1.329858  100000  \n",
       "  2016-11-09        1.322811  100000  \n",
       "  2016-11-10        1.337459  100000  \n",
       "  2016-11-11        1.347837  100000  \n",
       "  2016-11-14        1.352919  100000  \n",
       "  2016-11-15        1.352771  100000  \n",
       "  2016-11-16        1.352661  100000  \n",
       "  2016-11-17        1.355372  100000  \n",
       "  2016-11-18        1.347931  100000  \n",
       "  2016-11-21        1.357154  100000  \n",
       "  2016-11-22        1.367782  100000  \n",
       "  2016-11-23        1.370266  100000  \n",
       "  2016-11-24        1.375730  100000  \n",
       "  2016-11-25        1.388428  100000  \n",
       "  2016-11-28        1.393803  100000  \n",
       "  2016-11-29        1.405097  100000  \n",
       "  2016-11-30        1.394941  100000  \n",
       "  2016-12-01        1.405487  100000  \n",
       "  2016-12-02        1.391412  100000  \n",
       "  2016-12-05        1.368191  100000  \n",
       "  2016-12-06        1.364190  100000  \n",
       "  2016-12-07        1.370664  100000  \n",
       "  2016-12-08        1.368476  100000  \n",
       "  2016-12-09        1.377664  100000  \n",
       "  \n",
       "  [954 rows x 6 columns],\n",
       "  'portfolio':                 cash  market_value  static_unit_net_value  total_value  \\\n",
       "  date                                                                     \n",
       "  2013-01-08  2909.390       97013.0                  1.000    99922.390   \n",
       "  2013-01-09  2909.390       97823.0                  0.999   100732.390   \n",
       "  2013-01-10   160.390      100542.0                  1.007   100702.390   \n",
       "  2013-01-11   160.390      100829.0                  1.007   100989.390   \n",
       "  2013-01-14   160.390      103099.0                  1.010   103259.390   \n",
       "  2013-01-15   160.390      102955.0                  1.033   103115.390   \n",
       "  2013-01-16   160.390      103828.0                  1.031   103988.390   \n",
       "  2013-01-17   160.390      103949.0                  1.040   104109.390   \n",
       "  2013-01-18   160.390      102562.0                  1.041   102722.390   \n",
       "  2013-01-21   160.390      100056.0                  1.027   100216.390   \n",
       "  2013-01-22   444.322       98610.0                  1.002    99054.322   \n",
       "  2013-01-23   444.322       98842.0                  0.991    99286.322   \n",
       "  2013-01-24   444.322       96377.0                  0.993    96821.322   \n",
       "  2013-01-25   444.322       97429.0                  0.968    97873.322   \n",
       "  2013-01-28   444.322      103128.0                  0.979   103572.322   \n",
       "  2013-01-29   444.322      103701.0                  1.036   104145.322   \n",
       "  2013-01-30   444.322      102962.0                  1.041   103406.322   \n",
       "  2013-01-31    18.322      103508.0                  1.034   103526.322   \n",
       "  2013-02-01    18.322      107941.0                  1.035   107959.322   \n",
       "  2013-02-04    18.322      106102.0                  1.080   106120.322   \n",
       "  2013-02-05    18.322      107346.0                  1.061   107364.322   \n",
       "  2013-02-06    18.322      106271.0                  1.074   106289.322   \n",
       "  2013-02-07    18.322      105959.0                  1.063   105977.322   \n",
       "  2013-02-08    18.322      106131.0                  1.060   106149.322   \n",
       "  2013-02-18    18.322      105685.0                  1.061   105703.322   \n",
       "  2013-02-19    18.322      105972.0                  1.057   105990.322   \n",
       "  2013-02-20   429.130      106150.0                  1.060   106579.130   \n",
       "  2013-02-21   429.130      103870.0                  1.066   104299.130   \n",
       "  2013-02-22   429.130      100737.0                  1.043   101166.130   \n",
       "  2013-02-25   325.218      102359.0                  1.012   102684.218   \n",
       "  ...              ...           ...                    ...          ...   \n",
       "  2016-10-31  1152.091      170058.0                  1.720   171210.091   \n",
       "  2016-11-01  1152.091      171527.0                  1.712   172679.091   \n",
       "  2016-11-02  1152.091      168578.0                  1.727   169730.091   \n",
       "  2016-11-03  3252.983      169862.0                  1.697   173114.983   \n",
       "  2016-11-04  1997.363      172137.0                  1.731   174134.363   \n",
       "  2016-11-07  1997.363      172550.0                  1.741   174547.363   \n",
       "  2016-11-08  1997.363      172107.0                  1.745   174104.363   \n",
       "  2016-11-09  1346.531      170075.0                  1.741   171421.531   \n",
       "  2016-11-10   218.531      172585.0                  1.714   172803.531   \n",
       "  2016-11-11   218.531      173418.0                  1.728   173636.531   \n",
       "  2016-11-14  1322.421      175370.0                  1.736   176692.421   \n",
       "  2016-11-15    23.421      175662.0                  1.767   175685.421   \n",
       "  2016-11-16  1729.708      177983.0                  1.757   179712.708   \n",
       "  2016-11-17  1978.917      182035.0                  1.797   184013.917   \n",
       "  2016-11-18   819.917      182142.0                  1.840   182961.917   \n",
       "  2016-11-21   819.917      182193.0                  1.830   183012.917   \n",
       "  2016-11-22   819.917      182986.0                  1.830   183805.917   \n",
       "  2016-11-23   819.917      181760.0                  1.838   182579.917   \n",
       "  2016-11-24   819.917      182400.0                  1.826   183219.917   \n",
       "  2016-11-25   819.917      184235.0                  1.832   185054.917   \n",
       "  2016-11-28   819.917      186846.0                  1.851   187665.917   \n",
       "  2016-11-29    38.917      192097.0                  1.877   192135.917   \n",
       "  2016-11-30  4025.921      194042.0                  1.921   198067.921   \n",
       "  2016-12-01  4671.523      204064.0                  1.981   208735.523   \n",
       "  2016-12-02   383.523      200018.0                  2.087   200401.523   \n",
       "  2016-12-05   383.523      198004.0                  2.004   198387.523   \n",
       "  2016-12-06   383.523      197422.0                  1.984   197805.523   \n",
       "  2016-12-07   383.523      196328.0                  1.978   196711.523   \n",
       "  2016-12-08   383.523      204096.0                  1.967   204479.523   \n",
       "  2016-12-09   383.523      203909.0                  2.045   204292.523   \n",
       "  \n",
       "              unit_net_value   units  \n",
       "  date                                \n",
       "  2013-01-08        0.999224  100000  \n",
       "  2013-01-09        1.007324  100000  \n",
       "  2013-01-10        1.007024  100000  \n",
       "  2013-01-11        1.009894  100000  \n",
       "  2013-01-14        1.032594  100000  \n",
       "  2013-01-15        1.031154  100000  \n",
       "  2013-01-16        1.039884  100000  \n",
       "  2013-01-17        1.041094  100000  \n",
       "  2013-01-18        1.027224  100000  \n",
       "  2013-01-21        1.002164  100000  \n",
       "  2013-01-22        0.990543  100000  \n",
       "  2013-01-23        0.992863  100000  \n",
       "  2013-01-24        0.968213  100000  \n",
       "  2013-01-25        0.978733  100000  \n",
       "  2013-01-28        1.035723  100000  \n",
       "  2013-01-29        1.041453  100000  \n",
       "  2013-01-30        1.034063  100000  \n",
       "  2013-01-31        1.035263  100000  \n",
       "  2013-02-01        1.079593  100000  \n",
       "  2013-02-04        1.061203  100000  \n",
       "  2013-02-05        1.073643  100000  \n",
       "  2013-02-06        1.062893  100000  \n",
       "  2013-02-07        1.059773  100000  \n",
       "  2013-02-08        1.061493  100000  \n",
       "  2013-02-18        1.057033  100000  \n",
       "  2013-02-19        1.059903  100000  \n",
       "  2013-02-20        1.065791  100000  \n",
       "  2013-02-21        1.042991  100000  \n",
       "  2013-02-22        1.011661  100000  \n",
       "  2013-02-25        1.026842  100000  \n",
       "  ...                    ...     ...  \n",
       "  2016-10-31        1.712101  100000  \n",
       "  2016-11-01        1.726791  100000  \n",
       "  2016-11-02        1.697301  100000  \n",
       "  2016-11-03        1.731150  100000  \n",
       "  2016-11-04        1.741344  100000  \n",
       "  2016-11-07        1.745474  100000  \n",
       "  2016-11-08        1.741044  100000  \n",
       "  2016-11-09        1.714215  100000  \n",
       "  2016-11-10        1.728035  100000  \n",
       "  2016-11-11        1.736365  100000  \n",
       "  2016-11-14        1.766924  100000  \n",
       "  2016-11-15        1.756854  100000  \n",
       "  2016-11-16        1.797127  100000  \n",
       "  2016-11-17        1.840139  100000  \n",
       "  2016-11-18        1.829619  100000  \n",
       "  2016-11-21        1.830129  100000  \n",
       "  2016-11-22        1.838059  100000  \n",
       "  2016-11-23        1.825799  100000  \n",
       "  2016-11-24        1.832199  100000  \n",
       "  2016-11-25        1.850549  100000  \n",
       "  2016-11-28        1.876659  100000  \n",
       "  2016-11-29        1.921359  100000  \n",
       "  2016-11-30        1.980679  100000  \n",
       "  2016-12-01        2.087355  100000  \n",
       "  2016-12-02        2.004015  100000  \n",
       "  2016-12-05        1.983875  100000  \n",
       "  2016-12-06        1.978055  100000  \n",
       "  2016-12-07        1.967115  100000  \n",
       "  2016-12-08        2.044795  100000  \n",
       "  2016-12-09        2.042925  100000  \n",
       "  \n",
       "  [954 rows x 6 columns],\n",
       "  'stock_account':                 cash  dividend_receivable  market_value  total_value  \\\n",
       "  date                                                                   \n",
       "  2013-01-08  2909.390                  0.0       97013.0    99922.390   \n",
       "  2013-01-09  2909.390                  0.0       97823.0   100732.390   \n",
       "  2013-01-10   160.390                  0.0      100542.0   100702.390   \n",
       "  2013-01-11   160.390                  0.0      100829.0   100989.390   \n",
       "  2013-01-14   160.390                  0.0      103099.0   103259.390   \n",
       "  2013-01-15   160.390                  0.0      102955.0   103115.390   \n",
       "  2013-01-16   160.390                  0.0      103828.0   103988.390   \n",
       "  2013-01-17   160.390                  0.0      103949.0   104109.390   \n",
       "  2013-01-18   160.390                  0.0      102562.0   102722.390   \n",
       "  2013-01-21   160.390                  0.0      100056.0   100216.390   \n",
       "  2013-01-22   444.322                  0.0       98610.0    99054.322   \n",
       "  2013-01-23   444.322                  0.0       98842.0    99286.322   \n",
       "  2013-01-24   444.322                  0.0       96377.0    96821.322   \n",
       "  2013-01-25   444.322                  0.0       97429.0    97873.322   \n",
       "  2013-01-28   444.322                  0.0      103128.0   103572.322   \n",
       "  2013-01-29   444.322                  0.0      103701.0   104145.322   \n",
       "  2013-01-30   444.322                  0.0      102962.0   103406.322   \n",
       "  2013-01-31    18.322                  0.0      103508.0   103526.322   \n",
       "  2013-02-01    18.322                  0.0      107941.0   107959.322   \n",
       "  2013-02-04    18.322                  0.0      106102.0   106120.322   \n",
       "  2013-02-05    18.322                  0.0      107346.0   107364.322   \n",
       "  2013-02-06    18.322                  0.0      106271.0   106289.322   \n",
       "  2013-02-07    18.322                  0.0      105959.0   105977.322   \n",
       "  2013-02-08    18.322                  0.0      106131.0   106149.322   \n",
       "  2013-02-18    18.322                  0.0      105685.0   105703.322   \n",
       "  2013-02-19    18.322                  0.0      105972.0   105990.322   \n",
       "  2013-02-20   429.130                  0.0      106150.0   106579.130   \n",
       "  2013-02-21   429.130                  0.0      103870.0   104299.130   \n",
       "  2013-02-22   429.130                  0.0      100737.0   101166.130   \n",
       "  2013-02-25   325.218                  0.0      102359.0   102684.218   \n",
       "  ...              ...                  ...           ...          ...   \n",
       "  2016-10-31  1152.091                  0.0      170058.0   171210.091   \n",
       "  2016-11-01  1152.091                  0.0      171527.0   172679.091   \n",
       "  2016-11-02  1152.091                  0.0      168578.0   169730.091   \n",
       "  2016-11-03  3252.983                  0.0      169862.0   173114.983   \n",
       "  2016-11-04  1997.363                  0.0      172137.0   174134.363   \n",
       "  2016-11-07  1997.363                  0.0      172550.0   174547.363   \n",
       "  2016-11-08  1997.363                  0.0      172107.0   174104.363   \n",
       "  2016-11-09  1346.531                  0.0      170075.0   171421.531   \n",
       "  2016-11-10   218.531                  0.0      172585.0   172803.531   \n",
       "  2016-11-11   218.531                  0.0      173418.0   173636.531   \n",
       "  2016-11-14  1322.421                  0.0      175370.0   176692.421   \n",
       "  2016-11-15    23.421                  0.0      175662.0   175685.421   \n",
       "  2016-11-16  1729.708                  0.0      177983.0   179712.708   \n",
       "  2016-11-17  1978.917                  0.0      182035.0   184013.917   \n",
       "  2016-11-18   819.917                  0.0      182142.0   182961.917   \n",
       "  2016-11-21   819.917                  0.0      182193.0   183012.917   \n",
       "  2016-11-22   819.917                  0.0      182986.0   183805.917   \n",
       "  2016-11-23   819.917                  0.0      181760.0   182579.917   \n",
       "  2016-11-24   819.917                  0.0      182400.0   183219.917   \n",
       "  2016-11-25   819.917                  0.0      184235.0   185054.917   \n",
       "  2016-11-28   819.917                  0.0      186846.0   187665.917   \n",
       "  2016-11-29    38.917                  0.0      192097.0   192135.917   \n",
       "  2016-11-30  4025.921                  0.0      194042.0   198067.921   \n",
       "  2016-12-01  4671.523                  0.0      204064.0   208735.523   \n",
       "  2016-12-02   383.523                  0.0      200018.0   200401.523   \n",
       "  2016-12-05   383.523                  0.0      198004.0   198387.523   \n",
       "  2016-12-06   383.523                  0.0      197422.0   197805.523   \n",
       "  2016-12-07   383.523                  0.0      196328.0   196711.523   \n",
       "  2016-12-08   383.523                  0.0      204096.0   204479.523   \n",
       "  2016-12-09   383.523                  0.0      203909.0   204292.523   \n",
       "  \n",
       "              transaction_cost  \n",
       "  date                          \n",
       "  2013-01-08            77.610  \n",
       "  2013-01-09             0.000  \n",
       "  2013-01-10             5.000  \n",
       "  2013-01-11             0.000  \n",
       "  2013-01-14             0.000  \n",
       "  2013-01-15             0.000  \n",
       "  2013-01-16             0.000  \n",
       "  2013-01-17             0.000  \n",
       "  2013-01-18             0.000  \n",
       "  2013-01-21             0.000  \n",
       "  2013-01-22           120.068  \n",
       "  2013-01-23             0.000  \n",
       "  2013-01-24             0.000  \n",
       "  2013-01-25             0.000  \n",
       "  2013-01-28             0.000  \n",
       "  2013-01-29             0.000  \n",
       "  2013-01-30             0.000  \n",
       "  2013-01-31             5.000  \n",
       "  2013-02-01             0.000  \n",
       "  2013-02-04             0.000  \n",
       "  2013-02-05             0.000  \n",
       "  2013-02-06             0.000  \n",
       "  2013-02-07             0.000  \n",
       "  2013-02-08             0.000  \n",
       "  2013-02-18             0.000  \n",
       "  2013-02-19             0.000  \n",
       "  2013-02-20            13.192  \n",
       "  2013-02-21             0.000  \n",
       "  2013-02-22             0.000  \n",
       "  2013-02-25            13.912  \n",
       "  ...                      ...  \n",
       "  2016-10-31             0.000  \n",
       "  2016-11-01             0.000  \n",
       "  2016-11-02             0.000  \n",
       "  2016-11-03             7.108  \n",
       "  2016-11-04            11.620  \n",
       "  2016-11-07             0.000  \n",
       "  2016-11-08             0.000  \n",
       "  2016-11-09            12.832  \n",
       "  2016-11-10             5.000  \n",
       "  2016-11-11             0.000  \n",
       "  2016-11-14             6.110  \n",
       "  2016-11-15            10.000  \n",
       "  2016-11-16             6.713  \n",
       "  2016-11-17            11.791  \n",
       "  2016-11-18             5.000  \n",
       "  2016-11-21             0.000  \n",
       "  2016-11-22             0.000  \n",
       "  2016-11-23             0.000  \n",
       "  2016-11-24             0.000  \n",
       "  2016-11-25             0.000  \n",
       "  2016-11-28             0.000  \n",
       "  2016-11-29             5.000  \n",
       "  2016-11-30             8.996  \n",
       "  2016-12-01            14.398  \n",
       "  2016-12-02            10.000  \n",
       "  2016-12-05             0.000  \n",
       "  2016-12-06             0.000  \n",
       "  2016-12-07             0.000  \n",
       "  2016-12-08             0.000  \n",
       "  2016-12-09             0.000  \n",
       "  \n",
       "  [954 rows x 5 columns],\n",
       "  'stock_positions':             avg_price  last_price  market_value order_book_id  quantity  \\\n",
       "  date                                                                      \n",
       "  2013-01-08     61.490       61.49       49192.0   000895.XSHE     800.0   \n",
       "  2013-01-08     28.130       28.13       47821.0   000858.XSHE    1700.0   \n",
       "  2013-01-09     61.490       61.44       49152.0   000895.XSHE     800.0   \n",
       "  2013-01-09     28.130       28.63       48671.0   000858.XSHE    1700.0   \n",
       "  2013-01-10     61.490       61.60       49280.0   000895.XSHE     800.0   \n",
       "  2013-01-10      3.920        3.92        2744.0   600029.XSHG     700.0   \n",
       "  2013-01-10     28.130       28.54       48518.0   000858.XSHE    1700.0   \n",
       "  2013-01-11     61.490       61.51       49208.0   000895.XSHE     800.0   \n",
       "  2013-01-11      3.920        3.85        2695.0   600029.XSHG     700.0   \n",
       "  2013-01-11     28.130       28.78       48926.0   000858.XSHE    1700.0   \n",
       "  2013-01-14     61.490       64.40       51520.0   000895.XSHE     800.0   \n",
       "  2013-01-14      3.920        3.96        2772.0   600029.XSHG     700.0   \n",
       "  2013-01-14     28.130       28.71       48807.0   000858.XSHE    1700.0   \n",
       "  2013-01-15     61.490       64.55       51640.0   000895.XSHE     800.0   \n",
       "  2013-01-15      3.920        4.02        2814.0   600029.XSHG     700.0   \n",
       "  2013-01-15     28.130       28.53       48501.0   000858.XSHE    1700.0   \n",
       "  2013-01-16     61.490       66.49       53192.0   000895.XSHE     800.0   \n",
       "  2013-01-16      3.920        4.07        2849.0   600029.XSHG     700.0   \n",
       "  2013-01-16     28.130       28.11       47787.0   000858.XSHE    1700.0   \n",
       "  2013-01-17     61.490       66.68       53344.0   000895.XSHE     800.0   \n",
       "  2013-01-17      3.920        4.05        2835.0   600029.XSHG     700.0   \n",
       "  2013-01-17     28.130       28.10       47770.0   000858.XSHE    1700.0   \n",
       "  2013-01-18     61.490       65.99       52792.0   000895.XSHE     800.0   \n",
       "  2013-01-18      3.920        4.12        2884.0   600029.XSHG     700.0   \n",
       "  2013-01-18     28.130       27.58       46886.0   000858.XSHE    1700.0   \n",
       "  2013-01-21     61.490       64.42       51536.0   000895.XSHE     800.0   \n",
       "  2013-01-21      3.920        4.18        2926.0   600029.XSHG     700.0   \n",
       "  2013-01-21     28.130       26.82       45594.0   000858.XSHE    1700.0   \n",
       "  2013-01-22     20.480       20.48       43008.0   000001.XSHE    2100.0   \n",
       "  2013-01-22     61.490       64.20       51360.0   000895.XSHE     800.0   \n",
       "  ...               ...         ...           ...           ...       ...   \n",
       "  2016-12-02      4.749        6.84       98496.0   600050.XSHG   14400.0   \n",
       "  2016-12-02      7.633        7.57        3028.0   600029.XSHG     400.0   \n",
       "  2016-12-02     35.303       35.75       82225.0   000858.XSHE    2300.0   \n",
       "  2016-12-02      9.240        9.55        6685.0   000001.XSHE     700.0   \n",
       "  2016-12-02     12.011       11.98        9584.0   000402.XSHE     800.0   \n",
       "  2016-12-05      4.749        6.78       97632.0   600050.XSHG   14400.0   \n",
       "  2016-12-05      7.633        7.48        2992.0   600029.XSHG     400.0   \n",
       "  2016-12-05     35.303       35.54       81742.0   000858.XSHE    2300.0   \n",
       "  2016-12-05      9.240        9.46        6622.0   000001.XSHE     700.0   \n",
       "  2016-12-05     12.011       11.27        9016.0   000402.XSHE     800.0   \n",
       "  2016-12-06      4.749        6.64       95616.0   600050.XSHG   14400.0   \n",
       "  2016-12-06      7.633        7.29        2916.0   600029.XSHG     400.0   \n",
       "  2016-12-06     35.303       36.17       83191.0   000858.XSHE    2300.0   \n",
       "  2016-12-06      9.240        9.49        6643.0   000001.XSHE     700.0   \n",
       "  2016-12-06     12.011       11.32        9056.0   000402.XSHE     800.0   \n",
       "  2016-12-07      4.749        6.56       94464.0   600050.XSHG   14400.0   \n",
       "  2016-12-07      7.633        7.32        2928.0   600029.XSHG     400.0   \n",
       "  2016-12-07     35.303       36.20       83260.0   000858.XSHE    2300.0   \n",
       "  2016-12-07      9.240        9.48        6636.0   000001.XSHE     700.0   \n",
       "  2016-12-07     12.011       11.30        9040.0   000402.XSHE     800.0   \n",
       "  2016-12-08      4.749        7.01      100944.0   600050.XSHG   14400.0   \n",
       "  2016-12-08      7.633        7.30        2920.0   600029.XSHG     400.0   \n",
       "  2016-12-08     35.303       36.80       84640.0   000858.XSHE    2300.0   \n",
       "  2016-12-08      9.240        9.52        6664.0   000001.XSHE     700.0   \n",
       "  2016-12-08     12.011       11.16        8928.0   000402.XSHE     800.0   \n",
       "  2016-12-09      4.749        7.01      100944.0   600050.XSHG   14400.0   \n",
       "  2016-12-09      7.633        7.29        2916.0   600029.XSHG     400.0   \n",
       "  2016-12-09     35.303       36.66       84318.0   000858.XSHE    2300.0   \n",
       "  2016-12-09      9.240        9.65        6755.0   000001.XSHE     700.0   \n",
       "  2016-12-09     12.011       11.22        8976.0   000402.XSHE     800.0   \n",
       "  \n",
       "                  symbol  \n",
       "  date                    \n",
       "  2013-01-08      双汇发展    \n",
       "  2013-01-08       五粮液    \n",
       "  2013-01-09      双汇发展    \n",
       "  2013-01-09       五粮液    \n",
       "  2013-01-10      双汇发展    \n",
       "  2013-01-10      南方航空    \n",
       "  2013-01-10       五粮液    \n",
       "  2013-01-11      双汇发展    \n",
       "  2013-01-11      南方航空    \n",
       "  2013-01-11       五粮液    \n",
       "  2013-01-14      双汇发展    \n",
       "  2013-01-14      南方航空    \n",
       "  2013-01-14       五粮液    \n",
       "  2013-01-15      双汇发展    \n",
       "  2013-01-15      南方航空    \n",
       "  2013-01-15       五粮液    \n",
       "  2013-01-16      双汇发展    \n",
       "  2013-01-16      南方航空    \n",
       "  2013-01-16       五粮液    \n",
       "  2013-01-17      双汇发展    \n",
       "  2013-01-17      南方航空    \n",
       "  2013-01-17       五粮液    \n",
       "  2013-01-18      双汇发展    \n",
       "  2013-01-18      南方航空    \n",
       "  2013-01-18       五粮液    \n",
       "  2013-01-21      双汇发展    \n",
       "  2013-01-21      南方航空    \n",
       "  2013-01-21       五粮液    \n",
       "  2013-01-22      平安银行    \n",
       "  2013-01-22      双汇发展    \n",
       "  ...              ...    \n",
       "  2016-12-02      中国联通    \n",
       "  2016-12-02      南方航空    \n",
       "  2016-12-02       五粮液    \n",
       "  2016-12-02      平安银行    \n",
       "  2016-12-02       金融街    \n",
       "  2016-12-05      中国联通    \n",
       "  2016-12-05      南方航空    \n",
       "  2016-12-05       五粮液    \n",
       "  2016-12-05      平安银行    \n",
       "  2016-12-05       金融街    \n",
       "  2016-12-06      中国联通    \n",
       "  2016-12-06      南方航空    \n",
       "  2016-12-06       五粮液    \n",
       "  2016-12-06      平安银行    \n",
       "  2016-12-06       金融街    \n",
       "  2016-12-07      中国联通    \n",
       "  2016-12-07      南方航空    \n",
       "  2016-12-07       五粮液    \n",
       "  2016-12-07      平安银行    \n",
       "  2016-12-07       金融街    \n",
       "  2016-12-08      中国联通    \n",
       "  2016-12-08      南方航空    \n",
       "  2016-12-08       五粮液    \n",
       "  2016-12-08      平安银行    \n",
       "  2016-12-08       金融街    \n",
       "  2016-12-09      中国联通    \n",
       "  2016-12-09      南方航空    \n",
       "  2016-12-09       五粮液    \n",
       "  2016-12-09      平安银行    \n",
       "  2016-12-09       金融街    \n",
       "  \n",
       "  [2926 rows x 6 columns],\n",
       "  'summary': {'alpha': 0.141,\n",
       "   'annualized_returns': 0.2,\n",
       "   'benchmark': '000300.XSHG',\n",
       "   'benchmark_annualized_returns': 0.085,\n",
       "   'benchmark_total_returns': 0.378,\n",
       "   'beta': 0.812,\n",
       "   'cash': 383.523,\n",
       "   'downside_risk': 0.172,\n",
       "   'end_date': '2016-12-09',\n",
       "   'future_starting_cash': 0,\n",
       "   'information_ratio': 0.457,\n",
       "   'max_drawdown': 0.619,\n",
       "   'run_type': 'BACKTEST',\n",
       "   'sharpe': 0.615,\n",
       "   'sortino': 1.232,\n",
       "   'start_date': '2013-01-08',\n",
       "   'stock_starting_cash': 100000,\n",
       "   'strategy_file': 'strategy.py',\n",
       "   'strategy_name': 'strategy',\n",
       "   'total_returns': 1.043,\n",
       "   'total_value': 204292.523,\n",
       "   'tracking_error': 0.273,\n",
       "   'unit_net_value': 2.043,\n",
       "   'units': 100000,\n",
       "   'volatility': 0.343},\n",
       "  'trades':                      commission     exec_id  last_price  last_quantity  \\\n",
       "  datetime                                                                 \n",
       "  2013-01-08 15:00:00     38.2568  1496286000       28.13         1700.0   \n",
       "  2013-01-08 15:00:00     39.3536  1496286001       61.49          800.0   \n",
       "  2013-01-10 15:00:00      5.0000  1496286002        3.92          700.0   \n",
       "  2013-01-22 15:00:00     35.8496  1496286003       26.36         1700.0   \n",
       "  2013-01-22 15:00:00     34.4064  1496286004       20.48         2100.0   \n",
       "  2013-01-22 15:00:00      5.0000  1496286005       14.00          100.0   \n",
       "  2013-01-31 15:00:00      5.0000  1496286006        4.21          100.0   \n",
       "  2013-02-20 15:00:00      5.0000  1496286007        3.99          800.0   \n",
       "  2013-02-20 15:00:00      5.0000  1496286008       13.84          200.0   \n",
       "  2013-02-25 15:00:00      5.0000  1496286009       13.04          300.0   \n",
       "  2013-02-25 15:00:00      5.0000  1496286010       20.01          200.0   \n",
       "  2013-02-26 15:00:00      5.0000  1496286011        3.10          100.0   \n",
       "  2013-03-05 15:00:00      5.0000  1496286012        3.07          100.0   \n",
       "  2013-03-15 15:00:00     45.6960  1496286013       71.40          800.0   \n",
       "  2013-03-19 15:00:00     41.1600  1496286014       73.50          700.0   \n",
       "  2013-03-20 15:00:00      5.0000  1496286015       23.10          100.0   \n",
       "  2013-03-22 15:00:00      5.0000  1496286016        4.22          800.0   \n",
       "  2013-03-29 15:00:00     38.6304  1496286017       20.12         2400.0   \n",
       "  2013-03-29 15:00:00      5.0000  1496286018        4.05          800.0   \n",
       "  2013-04-03 15:00:00     40.6992  1496286019        3.66        13900.0   \n",
       "  2013-04-08 15:00:00      5.0000  1496286020        3.61          100.0   \n",
       "  2013-05-07 15:00:00      5.0000  1496286021        3.10          300.0   \n",
       "  2013-05-23 15:00:00     42.8288  1496286022       38.24         1400.0   \n",
       "  2013-05-23 15:00:00     42.2048  1496286023       23.98         2200.0   \n",
       "  2013-05-27 15:00:00      5.0000  1496286024        3.79          200.0   \n",
       "  2013-06-07 15:00:00      5.0000  1496286025       13.17          100.0   \n",
       "  2013-06-13 15:00:00     37.8048  1496286026       21.48         2200.0   \n",
       "  2013-06-13 15:00:00     40.2144  1496286027        3.54        14200.0   \n",
       "  2013-06-14 15:00:00      5.0000  1496286028        3.07          300.0   \n",
       "  2013-06-14 15:00:00      5.0000  1496286029       12.21          100.0   \n",
       "  ...                         ...         ...         ...            ...   \n",
       "  2016-09-13 15:00:00     63.0432  1496286390       17.91         4400.0   \n",
       "  2016-09-30 15:00:00     66.0520  1496286391       23.59         3500.0   \n",
       "  2016-10-12 15:00:00     65.8560  1496286392       34.30         2400.0   \n",
       "  2016-10-12 15:00:00     67.3552  1496286393        4.73        17800.0   \n",
       "  2016-10-14 15:00:00      5.0000  1496286394        5.17          500.0   \n",
       "  2016-10-19 15:00:00      5.0000  1496286395        5.30          200.0   \n",
       "  2016-10-20 15:00:00      5.0000  1496286396        5.30          100.0   \n",
       "  2016-10-21 15:00:00      5.0000  1496286397        5.36          100.0   \n",
       "  2016-10-24 15:00:00      5.1744  1496286398        9.24          700.0   \n",
       "  2016-10-28 15:00:00     62.9720  1496286399       22.49         3500.0   \n",
       "  2016-10-28 15:00:00     62.1808  1496286400       35.33         2200.0   \n",
       "  2016-11-03 15:00:00      5.0000  1496286401        5.27          400.0   \n",
       "  2016-11-04 15:00:00      5.0000  1496286402        7.16          400.0   \n",
       "  2016-11-04 15:00:00      5.0000  1496286403        5.40          300.0   \n",
       "  2016-11-09 15:00:00      5.0000  1496286404        7.08          400.0   \n",
       "  2016-11-09 15:00:00      5.0000  1496286405       34.70          100.0   \n",
       "  2016-11-10 15:00:00      5.0000  1496286406       11.23          100.0   \n",
       "  2016-11-14 15:00:00      5.0000  1496286407        5.55          200.0   \n",
       "  2016-11-15 15:00:00      5.0000  1496286408        7.47          100.0   \n",
       "  2016-11-15 15:00:00      5.0000  1496286409        5.42          100.0   \n",
       "  2016-11-16 15:00:00      5.0000  1496286410        5.71          300.0   \n",
       "  2016-11-17 15:00:00      5.0000  1496286411        7.65          200.0   \n",
       "  2016-11-17 15:00:00      5.0000  1496286412        5.97          300.0   \n",
       "  2016-11-18 15:00:00      5.0000  1496286413       11.54          100.0   \n",
       "  2016-11-29 15:00:00      5.0000  1496286414        7.76          100.0   \n",
       "  2016-11-30 15:00:00      5.0000  1496286415        6.66          600.0   \n",
       "  2016-12-01 15:00:00      5.0000  1496286416       12.46          300.0   \n",
       "  2016-12-01 15:00:00      5.0000  1496286417        7.33          600.0   \n",
       "  2016-12-02 15:00:00      5.0000  1496286418       11.98          300.0   \n",
       "  2016-12-02 15:00:00      5.0000  1496286419        6.84          100.0   \n",
       "  \n",
       "                      order_book_id    order_id position_effect  side  \\\n",
       "  datetime                                                              \n",
       "  2013-01-08 15:00:00   000858.XSHE  1496286000            None   BUY   \n",
       "  2013-01-08 15:00:00   000895.XSHE  1496286001            None   BUY   \n",
       "  2013-01-10 15:00:00   600029.XSHG  1496286002            None   BUY   \n",
       "  2013-01-22 15:00:00   000858.XSHE  1496286003            None  SELL   \n",
       "  2013-01-22 15:00:00   000001.XSHE  1496286004            None   BUY   \n",
       "  2013-01-22 15:00:00   600036.XSHG  1496286005            None   BUY   \n",
       "  2013-01-31 15:00:00   600029.XSHG  1496286007            None   BUY   \n",
       "  2013-02-20 15:00:00   600029.XSHG  1496286008            None  SELL   \n",
       "  2013-02-20 15:00:00   600036.XSHG  1496286009            None   BUY   \n",
       "  2013-02-25 15:00:00   600036.XSHG  1496286010            None  SELL   \n",
       "  2013-02-25 15:00:00   000001.XSHE  1496286011            None   BUY   \n",
       "  2013-02-26 15:00:00   600006.XSHG  1496286012            None   BUY   \n",
       "  2013-03-05 15:00:00   600006.XSHG  1496286013            None  SELL   \n",
       "  2013-03-15 15:00:00   000895.XSHE  1496286014            None  SELL   \n",
       "  2013-03-19 15:00:00   000895.XSHE  1496286015            None   BUY   \n",
       "  2013-03-20 15:00:00   000001.XSHE  1496286016            None   BUY   \n",
       "  2013-03-22 15:00:00   000089.XSHE  1496286017            None   BUY   \n",
       "  2013-03-29 15:00:00   000001.XSHE  1496286018            None  SELL   \n",
       "  2013-03-29 15:00:00   000089.XSHE  1496286019            None  SELL   \n",
       "  2013-04-03 15:00:00   600050.XSHG  1496286020            None   BUY   \n",
       "  2013-04-08 15:00:00   600050.XSHG  1496286021            None   BUY   \n",
       "  2013-05-07 15:00:00   600006.XSHG  1496286022            None   BUY   \n",
       "  2013-05-23 15:00:00   000895.XSHE  1496286023            None  SELL   \n",
       "  2013-05-23 15:00:00   000858.XSHE  1496286024            None   BUY   \n",
       "  2013-05-27 15:00:00   600050.XSHG  1496286025            None   BUY   \n",
       "  2013-06-07 15:00:00   600036.XSHG  1496286026            None   BUY   \n",
       "  2013-06-13 15:00:00   000858.XSHE  1496286027            None  SELL   \n",
       "  2013-06-13 15:00:00   600050.XSHG  1496286028            None  SELL   \n",
       "  2013-06-14 15:00:00   600006.XSHG  1496286029            None  SELL   \n",
       "  2013-06-14 15:00:00   600036.XSHG  1496286030            None  SELL   \n",
       "  ...                           ...         ...             ...   ...   \n",
       "  2016-09-13 15:00:00   600036.XSHG  1496286408            None  SELL   \n",
       "  2016-09-30 15:00:00   000895.XSHE  1496286409            None   BUY   \n",
       "  2016-10-12 15:00:00   601318.XSHG  1496286411            None  SELL   \n",
       "  2016-10-12 15:00:00   600050.XSHG  1496286412            None   BUY   \n",
       "  2016-10-14 15:00:00   600050.XSHG  1496286413            None  SELL   \n",
       "  2016-10-19 15:00:00   600050.XSHG  1496286414            None  SELL   \n",
       "  2016-10-20 15:00:00   600050.XSHG  1496286415            None  SELL   \n",
       "  2016-10-21 15:00:00   600050.XSHG  1496286416            None  SELL   \n",
       "  2016-10-24 15:00:00   000001.XSHE  1496286417            None   BUY   \n",
       "  2016-10-28 15:00:00   000895.XSHE  1496286418            None  SELL   \n",
       "  2016-10-28 15:00:00   000858.XSHE  1496286419            None   BUY   \n",
       "  2016-11-03 15:00:00   600050.XSHG  1496286420            None  SELL   \n",
       "  2016-11-04 15:00:00   600029.XSHG  1496286421            None   BUY   \n",
       "  2016-11-04 15:00:00   600050.XSHG  1496286422            None  SELL   \n",
       "  2016-11-09 15:00:00   600029.XSHG  1496286423            None  SELL   \n",
       "  2016-11-09 15:00:00   000858.XSHE  1496286424            None   BUY   \n",
       "  2016-11-10 15:00:00   000402.XSHE  1496286425            None   BUY   \n",
       "  2016-11-14 15:00:00   600050.XSHG  1496286426            None  SELL   \n",
       "  2016-11-15 15:00:00   600029.XSHG  1496286427            None   BUY   \n",
       "  2016-11-15 15:00:00   600050.XSHG  1496286428            None   BUY   \n",
       "  2016-11-16 15:00:00   600050.XSHG  1496286429            None  SELL   \n",
       "  2016-11-17 15:00:00   600029.XSHG  1496286430            None   BUY   \n",
       "  2016-11-17 15:00:00   600050.XSHG  1496286431            None  SELL   \n",
       "  2016-11-18 15:00:00   000402.XSHE  1496286432            None   BUY   \n",
       "  2016-11-29 15:00:00   600029.XSHG  1496286433            None   BUY   \n",
       "  2016-11-30 15:00:00   600050.XSHG  1496286434            None  SELL   \n",
       "  2016-12-01 15:00:00   000402.XSHE  1496286435            None   BUY   \n",
       "  2016-12-01 15:00:00   600050.XSHG  1496286436            None  SELL   \n",
       "  2016-12-02 15:00:00   000402.XSHE  1496286437            None   BUY   \n",
       "  2016-12-02 15:00:00   600050.XSHG  1496286438            None   BUY   \n",
       "  \n",
       "                           symbol     tax     trading_datetime  transaction_cost  \n",
       "  datetime                                                                        \n",
       "  2013-01-08 15:00:00       五粮液     0.000  2013-01-08 15:00:00           38.2568  \n",
       "  2013-01-08 15:00:00      双汇发展     0.000  2013-01-08 15:00:00           39.3536  \n",
       "  2013-01-10 15:00:00      南方航空     0.000  2013-01-10 15:00:00            5.0000  \n",
       "  2013-01-22 15:00:00       五粮液    44.812  2013-01-22 15:00:00           80.6616  \n",
       "  2013-01-22 15:00:00      平安银行     0.000  2013-01-22 15:00:00           34.4064  \n",
       "  2013-01-22 15:00:00      招商银行     0.000  2013-01-22 15:00:00            5.0000  \n",
       "  2013-01-31 15:00:00      南方航空     0.000  2013-01-31 15:00:00            5.0000  \n",
       "  2013-02-20 15:00:00      南方航空     3.192  2013-02-20 15:00:00            8.1920  \n",
       "  2013-02-20 15:00:00      招商银行     0.000  2013-02-20 15:00:00            5.0000  \n",
       "  2013-02-25 15:00:00      招商银行     3.912  2013-02-25 15:00:00            8.9120  \n",
       "  2013-02-25 15:00:00      平安银行     0.000  2013-02-25 15:00:00            5.0000  \n",
       "  2013-02-26 15:00:00      东风汽车     0.000  2013-02-26 15:00:00            5.0000  \n",
       "  2013-03-05 15:00:00      东风汽车     0.307  2013-03-05 15:00:00            5.3070  \n",
       "  2013-03-15 15:00:00      双汇发展    57.120  2013-03-15 15:00:00          102.8160  \n",
       "  2013-03-19 15:00:00      双汇发展     0.000  2013-03-19 15:00:00           41.1600  \n",
       "  2013-03-20 15:00:00      平安银行     0.000  2013-03-20 15:00:00            5.0000  \n",
       "  2013-03-22 15:00:00      深圳机场     0.000  2013-03-22 15:00:00            5.0000  \n",
       "  2013-03-29 15:00:00      平安银行    48.288  2013-03-29 15:00:00           86.9184  \n",
       "  2013-03-29 15:00:00      深圳机场     3.240  2013-03-29 15:00:00            8.2400  \n",
       "  2013-04-03 15:00:00      中国联通     0.000  2013-04-03 15:00:00           40.6992  \n",
       "  2013-04-08 15:00:00      中国联通     0.000  2013-04-08 15:00:00            5.0000  \n",
       "  2013-05-07 15:00:00      东风汽车     0.000  2013-05-07 15:00:00            5.0000  \n",
       "  2013-05-23 15:00:00      双汇发展    53.536  2013-05-23 15:00:00           96.3648  \n",
       "  2013-05-23 15:00:00       五粮液     0.000  2013-05-23 15:00:00           42.2048  \n",
       "  2013-05-27 15:00:00      中国联通     0.000  2013-05-27 15:00:00            5.0000  \n",
       "  2013-06-07 15:00:00      招商银行     0.000  2013-06-07 15:00:00            5.0000  \n",
       "  2013-06-13 15:00:00       五粮液    47.256  2013-06-13 15:00:00           85.0608  \n",
       "  2013-06-13 15:00:00      中国联通    50.268  2013-06-13 15:00:00           90.4824  \n",
       "  2013-06-14 15:00:00      东风汽车     0.921  2013-06-14 15:00:00            5.9210  \n",
       "  2013-06-14 15:00:00      招商银行     1.221  2013-06-14 15:00:00            6.2210  \n",
       "  ...                       ...       ...                  ...               ...  \n",
       "  2016-09-13 15:00:00      招商银行    78.804  2016-09-13 15:00:00          141.8472  \n",
       "  2016-09-30 15:00:00      双汇发展     0.000  2016-09-30 15:00:00           66.0520  \n",
       "  2016-10-12 15:00:00      中国平安    82.320  2016-10-12 15:00:00          148.1760  \n",
       "  2016-10-12 15:00:00      中国联通     0.000  2016-10-12 15:00:00           67.3552  \n",
       "  2016-10-14 15:00:00      中国联通     2.585  2016-10-14 15:00:00            7.5850  \n",
       "  2016-10-19 15:00:00      中国联通     1.060  2016-10-19 15:00:00            6.0600  \n",
       "  2016-10-20 15:00:00      中国联通     0.530  2016-10-20 15:00:00            5.5300  \n",
       "  2016-10-21 15:00:00      中国联通     0.536  2016-10-21 15:00:00            5.5360  \n",
       "  2016-10-24 15:00:00      平安银行     0.000  2016-10-24 15:00:00            5.1744  \n",
       "  2016-10-28 15:00:00      双汇发展    78.715  2016-10-28 15:00:00          141.6870  \n",
       "  2016-10-28 15:00:00       五粮液     0.000  2016-10-28 15:00:00           62.1808  \n",
       "  2016-11-03 15:00:00      中国联通     2.108  2016-11-03 15:00:00            7.1080  \n",
       "  2016-11-04 15:00:00      南方航空     0.000  2016-11-04 15:00:00            5.0000  \n",
       "  2016-11-04 15:00:00      中国联通     1.620  2016-11-04 15:00:00            6.6200  \n",
       "  2016-11-09 15:00:00      南方航空     2.832  2016-11-09 15:00:00            7.8320  \n",
       "  2016-11-09 15:00:00       五粮液     0.000  2016-11-09 15:00:00            5.0000  \n",
       "  2016-11-10 15:00:00       金融街     0.000  2016-11-10 15:00:00            5.0000  \n",
       "  2016-11-14 15:00:00      中国联通     1.110  2016-11-14 15:00:00            6.1100  \n",
       "  2016-11-15 15:00:00      南方航空     0.000  2016-11-15 15:00:00            5.0000  \n",
       "  2016-11-15 15:00:00      中国联通     0.000  2016-11-15 15:00:00            5.0000  \n",
       "  2016-11-16 15:00:00      中国联通     1.713  2016-11-16 15:00:00            6.7130  \n",
       "  2016-11-17 15:00:00      南方航空     0.000  2016-11-17 15:00:00            5.0000  \n",
       "  2016-11-17 15:00:00      中国联通     1.791  2016-11-17 15:00:00            6.7910  \n",
       "  2016-11-18 15:00:00       金融街     0.000  2016-11-18 15:00:00            5.0000  \n",
       "  2016-11-29 15:00:00      南方航空     0.000  2016-11-29 15:00:00            5.0000  \n",
       "  2016-11-30 15:00:00      中国联通     3.996  2016-11-30 15:00:00            8.9960  \n",
       "  2016-12-01 15:00:00       金融街     0.000  2016-12-01 15:00:00            5.0000  \n",
       "  2016-12-01 15:00:00      中国联通     4.398  2016-12-01 15:00:00            9.3980  \n",
       "  2016-12-02 15:00:00       金融街     0.000  2016-12-02 15:00:00            5.0000  \n",
       "  2016-12-02 15:00:00      中国联通     0.000  2016-12-02 15:00:00            5.0000  \n",
       "  \n",
       "  [420 rows x 12 columns]}}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#coding=utf-8\n",
    "# run_code_demo\n",
    "from rqalpha import run_code\n",
    "\n",
    "code = \"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import talib as ta\n",
    "import pandas as pd\n",
    "import os\n",
    "import rqalpha\n",
    "from rqalpha.api import *\n",
    "\n",
    "\n",
    "def init(context):\n",
    "    from datetime import timedelta\n",
    "    codes = pd.read_excel('C:/Users/small/Desktop/alpha.xlsx')\n",
    "    codes.index = codes.pop('date') + timedelta(hours=15)\n",
    "    context.codes = codes\n",
    "\n",
    "def find_pool(context, date):\n",
    "    codes = context.codes.loc[date]\n",
    "    return codes.index[codes == True]\n",
    "\n",
    "def handle_bar(context, bar_dict):\n",
    "    pool = find_pool(context, context.now)\n",
    "    print context.now\n",
    "    sell(context)\n",
    "    result = []\n",
    "    for codes in pool:\n",
    "        data_c = history_bars(codes, 25, '1d', 'close')\n",
    "        ma = ta.MA(data_c, timeperiod=20)\n",
    "        if ma[-1] > ma[-2] and ma[-2] > ma[-3]:\n",
    "            result.append(codes)\n",
    "    # print result\n",
    "    if len(result):\n",
    "        print result\n",
    "        for r in result:\n",
    "            order_target_percent(r, 0.5)\n",
    "\n",
    "\n",
    "def sell(context):\n",
    "    sell_list = []\n",
    "    for stocks in context.portfolio.positions:\n",
    "        data_c = history_bars(stocks, 25, '1d', 'close')\n",
    "        ma = ta.MA(data_c, timeperiod=20)\n",
    "        if ma[-1] < ma[-2] and ma[-2] < ma[-3]:\n",
    "            sell_list.append(stocks)\n",
    "    print('sell_list:', sell_list)\n",
    "    if len(sell_list):\n",
    "        for s in sell_list:\n",
    "            order_target_percent(s, 0)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "config = {\n",
    "  \"base\": {\n",
    "    \"start_date\": \"2013-01-08\",\n",
    "    \"end_date\": \"2016-12-11\",\n",
    "    \"securities\": ['stock'],\n",
    "    \"stock_starting_cash\": 100000,\n",
    "    \"benchmark\": \"000300.XSHG\"\n",
    "#     \"strategy_file_path\": os.path.abspath(__file__)\n",
    "  },\n",
    "  \"extra\": {\n",
    "    \"log_level\": \"verbose\",\n",
    "  },\n",
    "  \"mod\": {\n",
    "    \"sys_analyser\": {\n",
    "      \"enabled\": True,\n",
    "      \"plot\": True\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\n",
    "# 您可以指定您要传递的参数\n",
    "run_code(code, config)\n"
   ]
  }
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